Case Study: Cancer Surgery Wait Times Keep Climbing in the US

A decline inside a clinical success

In the 2012-2015 period, a US patient with breast cancer waited 34 days for surgery. In the 2022-2023 window, that same wait rose to 45 days.

This case study tells an uncomfortable story: an operational decline inside a system that, clinically, is advancing. Cancer therapies keep improving, survival outcomes keep rising, and meanwhile wait times for surgery keep stretching.

The data describes a widespread pattern. It covers six types of cancer and more than a decade of US clinical history. It is not an isolated anomaly at a single hospital. It is a trend measured on a national scale, consistent from one time window to the next.

The original idea: measuring before and after

The study, published in JAMA Surgery and led by Timothy Donahue of the University of California, Los Angeles, rests on a clean methodological choice. The researchers analyzed more than 2.7 million patients with stage I-III cancers diagnosed between 2012 and 2023.

They then split twelve years into four time windows: 2012-2015, 2016-2019, 2020-2021, and 2022-2023. This segmentation turns an impression into a verifiable comparison.

The value of the method lies here. Each window offers an explicit denominator, and the before/after comparison becomes legible to anyone examining the data. Without this breakdown, the lengthening of wait times would remain an anecdotal perception. With four distinct windows, instead, the trajectory becomes a data point that can be replicated and discussed.

Verified results: rising waits across six cancers

• Breast: from 34 to 45 days
• Colon: from 20 to 31 days
• Lung: from 41 to 53 days
• Pancreas: from 23 to 32 days
• Stomach: from 35 to 49 days
• Esophagus: from 38 to 48 days

The growth is incremental from one window to the next. The percentages of patients with prolonged waits (30 days and over) increased for all six cancers, as did the extreme waits of 60 days and over.

Why timeliness matters

The speed of access to surgery goes beyond the administrative plane. In cancer care it acts as a clinical variable.

Delays are associated with reduced survival for several cancers, and with increased anxiety and distress in patients who are waiting. Time, in this context, becomes part of the therapy.

That is why the clinical community regards timeliness as a central indicator of quality. An excellent outcome achieved late is worth less than the same outcome achieved early. The window preceding surgery is not neutral time: it is an interval in which the disease can progress and in which the patient bears a measurable emotional burden.

This frames the paradox of the case. The system invests in centers capable of superior outcomes, and meanwhile lengthens the time that precedes those outcomes.

The mechanism: centralization of care

Why do waits grow while medicine improves? The phenomenon coincides with a consolidation of US healthcare systems.

More patients are funneled toward high-volume centers, in particular specialized academic institutions. These centers deliver high-quality care, linked to better outcomes.

Here emerges the central tension of the case. The concentration of demand creates bottlenecks, and bottlenecks lengthen wait times. The clinical advantage of the specialized center risks being eroded by the delay that the very same influx generates. A limited number of operating rooms and specialists must absorb a growing volume of patients. Capacity does not expand at the same speed as concentrated demand grows.

The study poses a direct question: do the benefits of centralization hold when the wait doubles? It is the classic friction between excellence and capacity.

The friction that makes the case instructive

Every useful story contains a moment of friction. This one has it, and it is distributed unequally.

The authors note that the increase in wait times hits socioeconomically vulnerable patients harder. The risk of longer waits grows for those in specific categories.

• Patients without insurance or covered by Medicaid
• Black patients compared with white patients
• Low-income people
• Those facing a greater distance to reach the treatment center

This detail changes the nature of the problem. An average delay hides concrete inequalities, and the aggregate average turns out to be misleading. The useful correction starts precisely from this disaggregated reading. Anyone looking only at the average number concludes that the system slows uniformly. The disaggregated data instead shows that some patients pay for the delay more than others.

Where operational optimization comes in

The study's authors point to an operational way out. Centralized treatment centers can reduce wait times by increasing capacity and optimizing scheduling.

This is the point that interests those who build systems. The problem is one of scheduling, resource allocation, and demand forecasting. These are exactly the areas where optimization tools deliver measurable results.

An intelligent scheduling system assigns operating rooms, staff, and slots based on clinical priorities and real constraints. The gain is measured in wait-days recovered, with a clear before/after.

A concrete example: forecasting demand by department makes it possible to shift slots from saturated weeks to open ones. The same infrastructure reduces cancellations and idle time in the operating room.

The transferable lesson: the technology matters less than the architectural decision that makes it useful. A center that redesigns the scheduling flow achieves more than one that adds software on top of a fragile process. Software alone does not dissolve a bottleneck. What dissolves it is the decision to rethink how scarce resources are allocated.

What we can take away

The case is valuable beyond healthcare. Any organization that concentrates demand in a few high-quality nodes faces the same risk: excellence attracts volume, and volume generates queues.

For a founder or an SME CEO, the reading is practical. Before scaling toward a centralized model, measure the bottleneck's capacity with a before/after test on a single metric.

For a CTO or head of product, the message concerns real production. Scheduling optimization remains one of the most mature and verifiable AI applications, with a return readable in units of time.

For a board, the bar moves here: the quality of a service includes its timeliness. A benchmark that ignores the wait measures half the reality.

The open question

A valid question remains for every organization, inside and outside healthcare. How much of the quality we offer is consumed by the wait we impose to access it?

The study suggests that the answer comes from disaggregated data and from redesigned scheduling, rather than from new resources thrown on top of a saturated process. Anyone reading this case study can ask the same question about their own operational flow.

The willingness to rethink how time and capacity are allocated is a sign of operational maturity. And it is the move that separates a real center of excellence from an announced one. You can find more stories with the same pattern in our blog.